def post(self):
"""Serves requested file to the client."""
# Retrieve specified MRS data from the database.
mrs_data_id = self.request.get('mrs_data_id')
db_entry = ds.fetch_mrs_data(ds.create_sqlite_connection(), mrs_data_id)
# Set response headers.
self.response.headers['Content-Type'] = 'application/octet-stream'
self.response.headers['Content-Description'] = 'File Transfer'
self.response.headers['Content-Transfer-Encoding'] = 'binary'
self.response.headers['Content-Disposition'] = 'attachment; filename=\"%s\"' % db_entry[1]
self.response.headers['Content-Length'] = sys.getsizeof(db_entry[2])
# Set response content.
self.response.out.write(db_entry[2])
def prepare_mrs_data_set(self):
"""Retrieves all specified MRS data entries and processes each entry.
Each MRS file is parsed and FFT is applied if specified.
Returns:
Tuple containing (list of sample inputs, list of sample outputs).
"""
training_data_ids = self.request.get_all("training_data_ids")
apply_fft = 'apply_fft' in self.request.POST
LOGGER.debug('Processing MRS data: apply_fft=%s', apply_fft)
# Retrieve specified training data from the database.
conn = ds.create_sqlite_connection()
db_entries = [ds.fetch_mrs_data(conn, data_id) for data_id in training_data_ids]
# Separate each database entry into input and output.
sample_inputs = []
sample_outputs = []
for entry in db_entries:
# Parse data points from the file contents.
mrs_data = dataparser.get_xy_data(str(entry[2]))
# Apply FFT to the data points if specified by user.
if apply_fft:
mrs_data = fourier_transformer.get_fft(mrs_data)
# Add input, output pair to separate lists.
sample_inputs.append(mrs_data)
sample_outputs.append(entry[3])
# Format the data for classifier input.
n_samples = len(sample_inputs)
n_features = len(sample_inputs[0])
sample_inputs = np.array(sample_inputs) # convert before using as buffer
sample_inputs = np.ndarray(
shape=(n_samples, n_features), dtype=float, buffer=sample_inputs)
# Labels for training.
sample_outputs = np.array(sample_outputs)
# Return processed MRS data.
return (sample_inputs, sample_outputs)
